Multimodal quantitative analysis guides precise preoperative localization of epilepsy.

Area of Science:

• Neuroscience
• Medical Imaging
• Artificial Intelligence

Background:

• Epilepsy surgery success hinges on precise epileptogenic zone (EZ) localization.
• Current qualitative methods struggle with accuracy, multimodal data integration, and inter-practitioner variability.
• Advancements in AI and computing power enable multimodal quantitative analysis for improved EZ localization.

Purpose of the Study:

• To systematically review and synthesize recent advancements in multimodal quantitative analysis for EZ localization.
• To highlight the integration of seizure semiology, neuroimaging, functional imaging, and electrophysiology.
• To discuss challenges and future directions for data-driven EZ localization in epilepsy.

Main Methods:

• Review of literature on seizure semiology quantification (deep learning, computer vision).
• Analysis of structural neuroimaging (high-field MRI, radiomics, AI).
• Integration of functional imaging (EEG-fMRI, PET) and electrophysiological quantification (source localization, iEEG, network modeling).

Main Results:

• Multimodal quantitative analysis integrates behavioral, structural, functional, and electrophysiological data for comprehensive epileptogenic network characterization.
• Deep learning, advanced MRI techniques, and network modeling show potential for precise EZ localization.
• Convergence of these approaches offers a more robust understanding of epilepsy networks.

Conclusions:

• Multimodal quantitative analysis represents a significant advancement over qualitative methods for EZ localization.
• Clinical heterogeneity, algorithmic generalizability, and data sharing remain barriers to widespread adoption.
• Future research focusing on personalized modeling, federated learning, and standardization is crucial for clinical translation and improved patient outcomes.